Semi- vs. Fully-Distributed Urban Stormwater Models: Model Set Up and Comparison with Two Real Case Studies

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1 water Article Semi- vs. Fully-Distributed Urban Stormwater Models: Model Set Up Comparison Two Real Case Studies Rui Daniel Pa 1,2, *, Susana Ochoa-Rodriguez 1, Nuno Eduardo Simões 2, Ana Mijic 1, Alfeu Sá Marques 2 Čedo Maksimović 1 1 Department Civil Environmental Engeerg, Imperial College London, London SW7 2AZ, UK; s.ochoa-rodriguez@imperial.ac.uk (S.O.R.); ana.mijic@imperial.ac.uk (A.M.); c.maksimovic@imperial.ac.uk (C.M.) 2 MARE Mare Environmental Sciences Centre, Department Civil Engeerg, University Coimbra, Coimbra , Portugal; nunocs@dec.uc.pt (N.E.S.); jasm@dec.uc.pt (A.S.M.) * Correspondence: r.pa13@imperial.ac.uk; Tel.: Academic Editor: Andreas N. Angelakis Received: 27 November 2015; Accepted: 3 February 2016; Published: 16 February 2016 Abstract: Urban stormwater models can be semi-distributed (SD) or fully distributed (). SD models are based on subcatchment units various l use types, where rafall is applied runf are estimated routed. models are based on two dimensional (2D) discretization overl surface, which has a fer resolution each grid-cell representg one l use type, where runf are estimated directly routed by 2D overl flow module. While SD models have been commonly applied urban stormwater modelg, models are generally more detailed oretically more realistic. This paper presents a comparison between SD models usg two studies Coimbra (Portugal) London (UK). To enable direct comparison between SD setups, a model-buildg process is proposed a novel sewer let representation is applied. SD modelg results are compared agast observed records sewers photographic records flood events. results suggest that models are more sensitive to surface storage parameters require higher detail sewer network representation. Keywords: urban draage; urban pluvial floodg; urban stormwater models; fully-distributed models; semi-distributed models; rafall runf modelg 1. Introduction Urban stormwater models are simulation tools that clude algorithms methods to describe ma physical processes related to flow stormwater across urban catchments. y are usually based on couplg three ma modules: rafall runf, overl flow sewer flow. Rafall is ma data put rafall runf module that transms it to runf. Runf is n put to overl module, which routes flow over urban surface area, to sewer flow module, which accounts flow sewer system. Urban stormwater models can be considered semi-distributed (SD) or fully distributed (), dependg on spatial discretization rafall runf module. SD models are based on subcatchment units various l use types, where rafall is applied runf are estimated routed. In models, runf are estimated applied directly on elements a two-dimensional (2D) model overl surface. In SD models, conceptual empirical or physically based methods transm runf routg to flows hydrographs, which are applied to selected computational nodes sewer system. Not every let is modeled but y are clustered to Water 2016, 8, 58; doi: /w

2 Water 2016, 8, computational ones. models are based on a more realistic approach, sce generated grid-cell runf is directly routed 2D overl flow module. Traditional urban stormwater models have mostly been SD. One first widely implemented urban storm water models is Storm Water Management Model (SWMM) [1] an itial release It is based on tegration a rafall runf one-dimensional (1D) sewer flow modules, was itially developed to analyze combed sewers overflows [2]. Later on, Ellis et al. (1982) [3] troduced application overl flow module dual-draage concept, by couplg a 1D sewer flow module a 1D overl flow module that is known as 1D1D model. This concept was extended by Abbott (1993) [4] a 2D model overl flow, which is known as 1D2D model. However, use overl flow module only had major developments troduction Geographical Inmation Systems (GIS) end 1990s first decade At first, 1D1D models were significantly improved opened discussion about overl flow modelg [5 9]. In late 2000s, 1D2D models become more popular development technology crease computer power [10 13]. Noneless, rafall runf modules that have been usually applied urban stormwater modelg are commonly simplified SD models. models have been typically applied large-scale hydrology modelg, models like Mike SHE [14,15] MOHID L [16,17], amongst ors. In se large-scale applications, modeled catchments usually have a larger area than urban ones, coarser spatial resolution, models do not take to account urban features, such as buildgs curbs. Recent developments, however, brg new opportunities detailed physically based modelg urban stormwater systems. Examples important advancements are: crease available data (e.g., digital map [18], advanced collaborative sources mation [19], wear radar data [20]); advances technology (e.g., remote sensg [21], computg techniques [22]); improvements numerical methods (e.g., reduction simulation times 2D overl modelg [23], new mamatical approaches [24 26]). se improvements are openg discussion application urban stormwater models. Infoworks ICM [27] already implemented models, but its application has not yet become a stard practice water dustry. Bailey Margetts, 2008 [28] discussed potential models to replace limitations rafall runf ories adopted SD models. By analyzg a small study, authors achieved similar results SD models to demonstrate viability models, but y noted that models may still be computationally limited large scale catchments should require a significant amount detailed mation to represent all ro gully connections. Chang et al., 2015 [29] compared different approach setups 1D2D models applied to a mid-size real study. y compared flood extents permance dicators different models, concluded that a combation SD models is suitable approach analyzed study; however, y noted that models require mation which is seldom readily available pre-processg is ree needed to generate/estimate such mation (e.g., to defe buildg connections). This paper presents a full-scale comparison between SD urban stormwater models suggests novative concepts model buildg process, to establish connection between modules SD models. model buildg process proposed assigns same data to both SD models to enable a direct comparison two models. connection between modules accounts limited sewer let capacity, enable representation same teractions both SD models. comparison SD models were based on two real studies: catchment, London, UK; Zona catchment, Coimbra, Portugal. catchment has an area 8.5 km 2 a flat topography, hence surface water pondg is ma cause floodg. Zona is a very steep catchment an area 1.5 km 2 ma cause floodg is related sufficiency let capacity, i.e., overl gutter flow that cannot enter sewer system. Comprehensive detailed analyses modelg results were applied both studies. In catchment, modelg results were compared flows water depths records sewers. In Zona catchment, floodg extents have been analyzed based

3 Water 2016, 8, Water 2016, 8, on photographic records floodg events. Models were calibrated agast monitorg data catchment, floodg extents have been analyzed based on photographic records floodg events. photographic records floodg events. Furr analyses are presented design rafall events to Models were calibrated agast monitorg data photographic records floodg events. access importance surface storage both models. Furr analyses are presented design rafall events to access importance surface storage remader both models. paper is structured as follows: Section 2 presents sights to SD modelg approaches remader defes paper is structured conceptsas follows: modelsection buildg 2 presents to represent sights to SD teractions between modelg modules approaches SD defes models. concepts In Section model 3, buildg studies are to represent troduced teractions Section 4 presents between modules comprehensive SD detailed models. analysis In Section modelg 3, results. studies Section are troduced 5 presents Section discussion 4 presents conclusions comprehensive presented work. detailed analysis modelg results. Section 5 presents discussion conclusions presented work. 2. Semi- Fully-Distributed Modelg Approaches 2. Semi- concepts Fully-Distributed SD models Modelg areapproaches discussed this Section, followed by defition novative concepts model buildg SD process models are discussed new sewer this letsection, representation followed proposed by defition this work. model novative buildg model process buildg process sewer let concept new were sewer defed let representation studies proposed implemented this work. Infoworks model ICMbuildg v.5.5 stware process (Innovyze: sewer let Wallgd, concept were UK) defed [27] can be replicated studies implemented any urban stormwater Infoworks modelg ICM v.5.5 package. stware (Innovyze: Wallgd, UK) [27] can be replicated any urban stormwater modelg package Conceptual Basis Semi-Distributed Fully Distributed Models 2.1. Conceptual Basis Semi-Distributed Fully Distributed Models SD models are based on defition subcatchment units, deleated based upon analysis areas SD models drag are based towards on a given defition discharge subcatchment pot ( units, 1a). deleated This discharge based upon pot analysis referred to as areas subcatchment drag towards outlet, a given it is represented discharge pot by a( computational 1a). This discharge node pot usually is referred corresponds to as to a node subcatchment sewer outlet, system. it is represented Each subcatchment by a computational unit is approximated node usually by acorresponds regularly shaped to a surface node to which sewer unim system. morphological Each subcatchment hydrological unit is approximated characteristics by a are regularly assigned shaped (e.g., surface area, mean to slope, which imperviousness, unim morphological filtration hydrological properties). characteristics A spatially unim are assigned rafall (e.g. put area, mean is assigned slope, to imperviousness, filtration properties). A spatially unim rafall put is assigned to each each subcatchment. Runf are estimated subcatchment are n routed to subcatchment. Runf are estimated subcatchment are n routed to subcatchment outlet by means a conceptual or physically-based model. result this process subcatchment outlet by means a conceptual or physically-based model. result this process are runf hydrographs at subcatchments outlets. SD models can be implemented 1D, 1D1D are runf hydrographs at subcatchments outlets. SD models can be implemented 1D, 1D1D 1D2D models. 1D2D models. models are defed by a 2D overl mesh discretization ( 1b). rafall is directly models are defed by a 2D overl mesh discretization ( 1b). rafall is directly applied applied to to each each 2D 2D element, element, generatg generatg grid-pot grid-pot runf, runf, routg routg surface surface runf runf is is n n simulated simulated directly directly by by 2D 2D overl overl flow flow module. ree, models modelsare arephysically-based that that can can replicate replicate runf runf processes processes more more realistically. Moreover, because type type discretization, models models can can only only be be applied 2D 2D overl flow modules (1D2D models). (a) (b) 1. Semi-distributed (a) fully-distributed (b) models. 1. Semi-distributed (a) fully-distributed (b) models. ma differences between SD models are related to rafall losses calculation (itial contug ma differences losses) between runf SD routg. y models can be are summarized related to as rafall follows. losses calculation (itial contug losses) runf routg. y can be summarized as follows.

4 Water 2016, 8, Initial losses: ma difference is related to representation depression storage. Depression storage is stormwater that is retaed small depressions on overl surface (puddle mg) pores surface materials, both impervious pervious areas (surface wettg) [30]. In SD models, se two phenomena are usually considered a constant value or a sgle value that is subtracted directly from rafall is dependent on subcatchments slope surface type [31]. In models, due to fer resolution, overl flow module can account more detailed depressions that orig puddle mg [28]. Contug losses: ma difference is related to filtration modelg. Infiltration is percentage rafall drag to soil. In SD models, filtration is estimated each subcatchment based on soil saturation, subtracted from rafall bee beg applied to model. In models, rafall is applied directly to overl mesh filtration is estimated each 2D element, based on soil saturation water depth. ree, filtration predicted by models takes to account runf quantity on overl surface, can capture filtration to permeable surfaces runf routed from upstream impermeable areas. Runf routg: In SD models, generated runf is transmed by rafall runf module to an flow hydrograph that is usually applied to sewer flow module. In models, generated runf is directly applied to overl flow module routed overl surface. SD runf routg functions are based on both physically based as well as empirical or conceptual methods, resolutions defed by subcatchments sizes [32,33]. runf routg is simulated by applyg physically based approaches resolutions defed by surface overl mesh. While models enable representation real connection between impervious pervious areas on surface, SD models usually merge runf discharges to sewers from impervious pervious area subcatchments, unless subcatchments are eir pervious or impervious. In addition, runf captured by surface ponds are captured by models, sce y consider runf on overl mesh, whereas SD models can neglect se dependg on subcatchment deleation ir discharge defition Model Buildg Process proposed model buildg process was defed to assign exactly same data to both SD models. While 1D sewer flow 2D overl flow modules are equally set up both models (both SD models can be based on same 1D2D model), rafall runf module needs a different procedure to assign data to subcatchments SD models, to overl surface mesh models. procedure proposed is based on assigng percentages l use types each subcatchment ( SD model) a l use category each element overl surface mesh ( model) ( 2). In SD models, each subcatchment is defed by percentage l use cover (e.g., has a percentage area covered by road, parks, etc., each surface havg modelg attributes defed l use type). In models, each mesh element is characterized by one l use type (e.g., can be considered road or park correspondg properties). 2D mesh should be deleated considerg boundaries l use polygons to ensure that each 2D mesh element has only one l use type. Buildgs polygons can be considered as voids 2D mesh, ir ro runf is modeled model as subcatchments that discharge directly to sewer network to take to account private connections. This procedure guarantees put same data both SD models, despite ir different spatial resolution.

5 Water 2016, 8, Water 2016, 8, SD SD (Semi-distributed) (fully distributed) rafall runf l use assignment Connections between Modules Inlet Capacity amount water enterg sewer sewer system system is, is, reality, reality, limited limited by by capacity capacity sewer sewer lets; lets; noneless, noneless, this fact this fact not is always not always considered considered urban urban draage draage models. models. SD models SD models can take can take to to account account let capacity let capacity if if subcatchments subcatchments are deleated are deleated each sewer each let. sewer However, let. However, SD models SD models usually apply usually apply runf all estimated runf estimated a given subcatchment a given subcatchment directly to directly selected to computational selected computational node sewer node system, out sewer accountg system, out let accountg capacity ( let 3a). Neglectg capacity ( limited 3a). Neglectg capacity lets limited meanscapacity that model lets fails means to represent that model stormwater fails pondg represent floodg stormwater that pondg may occur due floodg to limited that let may capacity, occur due even to limited bee runf let capacity, reaches even sewer bee system. runf reaches As a result, sewer floodg system. only occurs As a result, whenfloodg sewer only system occurs surcharges. when sewer system surcharges. modelg packages, such such as Infoworks as ICM ICM v.5.5 v.5.5 stware stware [27], have [27], cluded have cluded let capacity let capacity sewer lets sewer network lets nodes network connected nodes connected 2D overl 2D surface overl mesh surface ( mesh 3b). ( In general, 3b). In a weir general, or orifice a weir equation or orifice is defed equation is defed manhole to control manhole to let control capacity let capacity water level on water surface. level on surface. To overcome limited representation sewer lets SD models, a concept based on virtual nodes was developed, as represented 3c. se virtual nodes have an fitesimal volume, are directly connected overl overl surface surface subcatchments. subcatchments. y are y also connected are also connected sewer network sewer manholes network through manholes orifices through orifices limited capacity limited sewer capacity lets. ree, sewer lets. flow ree, to sewer flow systemto from subcatchments sewer system discharges from subcatchments overldischarges module is limited overl by module let capacity is limited gullies by defed let capacity by discharge gullies curve defed orifices. by discharge If let curve capacity orifices. is exceeded, If let runf capacity remasis onexceeded, overl runf surface, remas as it on cannot enter overl sewer surface, systems. as it cannot In addition, enter flap valves sewer were systems. adopted In addition, opposite flap valves direction were adopted orifices to enable opposite runf direction to flow from orifices sewers to enable onto runf 2D to flow surface from model sewers once sewer onto surcharge 2D surface occurs. model discharge once sewer curve surcharge that defes occurs. sewer lets discharge capacity curve is that baseddefes on recommendations sewer lets capacity presented is based by Pa on et recommendations al. (2010) [34] presented Ally (2011) by [35]. Pa et al. (2010) [34] Ally To consider (2011) [35]. same let capacity SD models, representation sewer lets models To was consider based on an same equivalent let capacity concept as defed SD SDmodels, but representation out subcatchments sewer ( lets 3d). models sewer let was concept based on typically an equivalent defed concept models as defed ( SD 3b) models was not but adopted out tosubcatchments guarantee ( same connections 3d). sewer between let modules concept typically both SDdefed models, models makg ( m3b) comparable. was not adopted to guarantee same connections between modules both SD models, makg m comparable.

6 Water 2016, 8, Water 2016, 8, (a) (b) (c) (d) 3. Connections rafall runf overl sewer flow modules: (a) traditional 3. Connections rafall runf overl sewer flow modules: (a) traditional connections SD models; (b) traditional connections models; (c) developed connections connections SD models; (b) traditional connections models; (c) developed connections SD models; (d) developed connections models. SD models; (d) developed connections models. 3. Case Studies 3. Case Studies selected studies are catchment, London, UK, Zona catchments, selected Coimbra, studies Portugal. are For each catchment, study, SD London, models UK, were implemented Zona Infoworks catchments, ICM Coimbra, v. 5.5 [27] based Portugal. on For same each 1D sewer study, network SD 2D models overl were flow implemented models (1D2D models). Infoworks To ICM enable v. 5.5 comparison [27] based on between same both 1D sewer studies, network similar data 2D overl were collected flow models to build (1D2D models. models). To enable sewer flow comparison model was between built both network studies, similar operational data were data, collected provided to build by respective models. water sewer companies flow model was study built catchments. network 2D overl operational flow model data, was provided created by based on respective available water LiDAR-based companies Digital study Terra catchments. Models (DTM) 2D overl 1 m horizontal flow modelresolution. was createdbuildgs based on polygons available LiDAR-based l use Digital data were Terra used Models to characterize (DTM) 1 m horizontal model (e.g., resolution. roughness Buildgs filtration polygons parameters) l use data to were defe used surface to characterize mesh (e.g. mesh model resolution, (e.g., roughness break les, filtration voids, boundaries). parameters) to l defe use data surface were mesh obtaed (e.g., from mesh resolution, OpenStreetMap break les, [19] voids, buildgs boundaries). polygons were l provided use data were by local obtaed authorities. from OpenStreetMap SD models [19] se buildgs studies polygons have been were developed provided by updated local authorities. sce 2010 SD2009, models respectively se [34,36]. studies se have SD been models developed were improved updated sce calibrated 2010 followg 2009, respectively UK stards [34,36]. [37] se usg SD local models rafall were improved flow records. calibrated model followg both UK studies stards was [37] developed usg local rafall exact flow records. same data as model calibrated both SD model, studies was followg developed methodology exact presented same data as Section calibrated 2, so as SDto model, achieve followg comparable methodology models. presented Section 2, so as to achieve comparable models study 3.1. Case Study catchment is located North-East part London, UK, is presented catchment is located North-East part London, UK, is presented 4. It is predomantly urban (residential commercial units), some open green spaces. 4. It is predomantly urban (residential commercial units), some open green spaces. It covers an area 8.5 km 2 an average slope 5%. stormwater sewer system is nearly 98 km It covers an area 8.5 km long; it is maly separate 2 an average slope 5%. stormwater sewer system is nearly 98 km discharges to Rodg River. This catchment has suffered several long; it is maly separate discharges to Rodg River. This catchment has suffered several floods durg recent years (e.g., ), which have affected hundreds properties. floods durg recent years (e.g., ), which have affected hundreds properties. real time monitorg system has been operated catchment sce April 2010 A real time monitorg system has been operated catchment sce April 2010 ( 4b). It cludes four ra gauges, three water level sensors (one sewers two channels) ( 4b). It cludes four ra gauges, three water level sensors (one sewers two channels) two flow gauges sewers that record water depth velocity. most upstream sensor two flow gauges sewers that record water depth velocity. most upstream sensor (Barkgside) was stalled December 2014, covers a limited area km 2 that is mostly (Barkgside) was stalled December 2014, covers a limited area 2 km residential. Valente sewer Valente channel sensors are located almost 2 that is mostly middle residential. Valente sewer Valente channel sensors are located almost middle catchment, upstream draage areas km 2, respectively. As names suggest, one catchment, upstream draage areas km sensor is stalled sewers enterg Valente Park 2, respectively. As names suggest, one or on an open channel sensor is stalled sewers enterg Valente Park or on an open channel Park. Park. sewer is a sensor stalled downstream area covers most sewer is a sensor stalled downstream area covers most catchment area catchment area (8.0 km 2 ). re is also a level gauge ma discharge catchment to (8.0 km validate 2 ). re is also a level gauge ma discharge catchment to validate outfall outfall conditions, sce y can be fluenced by level Rodg River. conditions, sce y can be fluenced by level Rodg River.

7 Water 2016, 8, Water 2016, 8, (b1) (b2) (a) (b3) (b) (b4) study London, study London, United United Kgdom: Kgdom: (a) (a) DTM DTM (Digital (Digital Terra Terra Model) Model) network network data. data. (b) (b) Monitorg Monitorg stations stations upstream upstream network: network: (b1) (b1) Barkgside Barkgside (flow (flow depth depth sensor); sensor); (b2) (b2) Valente Valente sewer sewer (depth (depth sensor); sensor); (b3) Valente (b3) Valente channel channel (depth sensor); (depth sensor); (b4) (b4) sewer (flowsewer depth (flow sensor). depth sensor). SD models study clude 1D network based on a sewer SD models study clude a 1D network based on a sewer system 2596 conduits 2546 manhole nodes. conduits have an average slope 1% system 2596 conduits 2546 manhole nodes. conduits have an average slope 1% cross sections diameters rangg from 100 mm to 1950 mm. 1D network also cludes 565 m cross sections diameters rangg from 100 mm to 1950 mm. 1D network also cludes 565 m open channels cross sections up to 6 m width, five storage ponds, four which are open channels cross sections up to 6 m width, five storage ponds, four which are recreational lakes. SD rafall runf model has 4409 subcatchments areas rangg from 50 m 2 recreational lakes. SD rafall runf model has 4409 subcatchments areas rangg from to 40 ha, average 0.2 ha; slopes are varyg from m/m to m/m an average 50 m to 40 ha, average 0.2 ha; slopes are varyg from m/m to m/m an average m/m, widths are rangg from 4 m to 357 m, an average 22 m. It considers itial 0.05 m/m, widths are rangg from 4 m to 357 m, an average 22 m. It considers itial losses dependent on subcatchments slopes Wallgd routg model. Infiltration losses losses dependent on subcatchments slopes Wallgd routg model. Infiltration losses are estimated both SD models fixed runf coefficients. overl flow module, are estimated both SD models fixed runf coefficients. overl flow module, which defes resolution model, is based on a 2D mesh 117,712 elements which defes resolution model, is based on a 2D mesh 117,712 elements areas areas rangg from 25 m 2 to 992 m 2 mean 61 m 2. rangg from 25 m 2 to 992 m 2 mean 61 m Zona study 3.2. Zona Case Study Zona catchment is located Coimbra, Portugal ( 5). It covers highly Zona catchment is located Coimbra, Portugal ( 5). It covers highly urbanized urbanized zones, maly residential commercial, cludg downtown area Coimbra, zones, maly residential commercial, cludg downtown area Coimbra, where important where important services historical buildgs are located. It has a total draage area services historical buildgs are located. It has a total draage area approximately 1.5 km 2 approximately 1.5 km 2 an average slope 24%. sewer system is nearly 35 km long, most an average slope 24%. sewer system is nearly 35 km long, most which is combed which is combed discharges to Coselhas brook to Coimbra Waste Water discharges to Coselhas brook to Coimbra Waste Water Treatment Plant, from where it is Treatment Plant, from where it is furr directed to Mondego River. This catchment has suffered furr directed to Mondego River. This catchment has suffered several floods durg recent years, several floods durg recent years, occurrence which is exacerbated by steep topography limited let capacity sewer system. area at highest risk floodg is Praça 8 de Maio ( 5b), a square center catchment, where important services are located

8 Water 2016, 8, occurrence Water 2016, 8, 2 which is exacerbated by steep topography limited let capacity 8 sewer 20 system. area at highest risk floodg is Praça 8 de Maio ( 5b), a square center (e.g., catchment, City Council where important tourist attractions) services are located where flood (e.g., City waters Council tend to pond tourist due to attractions) topographic where conditions. flood waters tend to pond due to topographic conditions. (a) (b) Zona Zona catchment Coimbra, catchment Coimbra, Portugal: Portugal: (a) (a) sewer sewer network, network, DTM DTM monitorg monitorg pot pot locations; (b) extents Praça 8 de Maio. locations; (b) extents Praça 8 de Maio. A monitorg campaign was conducted this catchment between by Simões, 2012 A [36]. monitorg campaign campaign cluded was conducted three ra gauges this catchment two water between depth 2010 sensors latter by Simões, were 2012 located [36]. along campaign ma sewer, cluded upstream three ra gauges Praça 8 de Maio, two coverg water depth draage sensors. areas 0.4 latter km were 2 located Mercado along station ma sewer, 1.0 upstream km 2 Praça Praça da Républica 8 de Maio, gauges coverg ( draage 5a), respectively. areas 0.4In km 2 addition, Mercado water utility 1.0 km area AC, 2 Águas Praçade dacoimbra Républica E.M. has gauges mataed ( 5a), a sgle respectively. ra Ingauge addition, catchment water utility several area AC, years (sce Águas approximately de Coimbra2005); E.M. has from this mataed gauge contuous a sgle ra gauge rafall records catchment are available, several cludg years (sce records approximately flood-generatg 2005); storms. from this gauge data contuous collected rafall between records 2010 are available, 2012 were cludg used to calibrate records flood-generatg SD model storms. ra gauge data records collected collected between by 2010 Águas de 2012 Coimbra were used are used to calibrate as put SD model flood simulations ra presented gauge records this paper. collected by Águas de Coimbra are SD used as put models flood Zona simulations presented study are based this on paper. a 1D sewer network model comprisg SD 1016 conduits models 1014 Zona manhole nodes. study conduits are based have on an aaverage 1D sewer slope network 5% model comprisg cross-sections 1016 conduits dimensions 1014 rangg manhole from 200 nodes. mm circular conduits diameter have to an closed average rectangular slope section 5% cross-sections dimensions 3.5 dimensions 1.7 m 2. rangg SD rafall runf from 200 mm model circular has 911 diameter subcatchments to closed rectangular areas rangg section from dimensions 50 m 2 to ˆha 1.7 ma 2. mean SD 1722m rafall runf 2, slopes rangg model from has subcatchments m/m to 1.13 m/m areas a mean rangg from m/m, m 2 to 4.8 widths ha rangg a mean from 1722m 6 m to 2, slopes 493 m rangg a mean from m. m/m In to SD 1.13 model, m/m itial a losses mean are given as an absolute value runf are routed to subcatchments outlets usg 0.24 m/m, widths rangg from 6 m to 493 m a mean 51 m. In SD model, itial losses SWMM routg model. For both SD models, filtration losses are estimated Horton are given as an absolute value runf are routed to subcatchments outlets usg equation pervious areas, whereas a fixed runf coefficients approach was adopted SWMM routg model. For both SD models, filtration losses are estimated Horton impervious areas. overl flow module, which defes resolution model, is based equation pervious areas, whereas a fixed runf coefficients approach was adopted impervious on a 2D mesh 10,741 elements, areas rangg from 25 m 2 to 678 m 2, a mean 89 m 2. areas. overl flow module, which defes resolution model, is based on a 2D mesh 4. Results 10,741 elements, Discussion areas rangg from 25 m 2 to 678 m 2, a mean 89 m Results Case Discussion Study 4.1. analysis Case Study study was based on three selected events, which rafall water depth flow records sewers were available. rafall records are summarized analysis study was based on three selected events, which rafall Table 1, considerg average rafall entire catchment area. For each event, entire day was water simulated, depth but only flow records time frame sewers correspondg were available. to ma rafall rafall records event are was summarized analyzed to Table mimize 1, considerg errors related average to impact rafall itial entire conditions. catchment area. For each event, entire day was simulated, but only time frame correspondg to ma rafall event was analyzed to mimize errors related to impact itial conditions.

9 Water 2016, 8, Water 2016, 8, 58 Table 1. Summary rafall records used study Maximum Event Duration Total rafall Average rafall Start Table 1. Summary End rafall records used rafall study. ID* (h) depth (mm) tensity (mm/h) (mm/h) 12 December December Duration Maximum Total Rafall Average Rafall Event ID * Start End :30 a.m. 8:00 a.m. (h) Rafall (mm/h) Depth (mm) Intensity (mm/h) January January December 12 December : a.m. 1:30 A.M. 8:00 5:00 A.M. p.m January 3 January January :303:50 a.m. A.M. 2:30 5:00 P.M. p.m January January Note: * This code 7:30represents A.M. yymmdd 2:30 P.M. it is used next figures to reference se events. 2 Note: * This code represents yymmdd it is used next figures to reference se events. balances presented 6 show distribution among modules all events balances analyzed. presented Runf 6were showgenerated distribution by rafall runf among module, modules y were all calculated events analyzed. subcatchments Runf discharges were generated SD models, by rafall runf runf module, volume y generated were calculated on 2D mesh subcatchments models. Volumes discharges SDoverl models, flow module runf were volume calculated generated on difference 2D mesh between models. Volumes at end overl simulation flow module ones wereat calculated begng each difference event. between at sewer end flow system simulation were defed ones by at discharges begngat outfalls each event. 1D sewer network. sewer flow system were defed by discharges at outfalls 1D sewer network. In all all three three events, events, total total runf runf are similar are similar to total combed total combed from overl from overl sewer flowsewer modules. flow modules. significant significant differences differences total runf total runf are caused by small are differences caused by small subcatchments differences areas subcatchments SD model areas when compared SD model to when model. compared However, to all simulations model. However, model all retaed simulations more water model on retaed surfacemore contrast water on to SD model, surface where contrast most to SD runf model, iswhere conveyed most through runf draage is conveyed system. through draage system Volume Volume balance balance study study model model runs. runs. To furr explore source water accumulation on overl surface, differences between SD maximum at surface were divided by l use groups ( 7). most significant differences are observed are observed roads roads buildgs buildgs (residential, (residential, retail retail dustrial areas) dustrial zones, areas) leadg zones, to leadg conclusion to that conclusion runf onthat runf overl on overl models is retaed models due to is surface retaed pondg due to surface buildg pondg sgularities. buildg category sgularities. Or areas category cludes Or non-classified areas cludes zones non-classified l usezones data that cover l a mix use data between that open cover areas a mix between zonesopen covered areas by buildgs. zones covered by buildgs.

10 Water 2016, 8, 58 Water 2016, 8, 2 Water 2016, 8, Water 2016, 8, on surface generated by SD by SD models, Differences Differencesbetween betweenrunf runf on overl overl surface generated 7. Differences between runf on overl surface generated by SD distributed by l use groups study. bars correspond three storm models, distributed by groups l use study. bars to correspond to events three models,7.distributed l userunf groups study.surface bars correspond to Differencesbybetween on overl generated by SD three under events consideration (see Table 1). (see Table 1). storm under consideration storm events under consideration (see Table 1). models, distributed by l use groups study. bars correspond to three storm events under consideration (see Table 1). As As water sewer flow module are generally lower than SD models, As water water sewer sewer flow flow module module are are generally generally lower lower than than SD SD models, models, results tend to underestimate water depth flows sewers, as exemplified s 8 99 results totounderestimate depth flows generally sewers, aslower exemplified s 89 results tend underestimate water depth flows sewers, as exemplified Astend water water sewer flow module are than s SD8models, two monitorg locations Event se figures also show correct calibration two locations Event se figures also show correct calibration 8 itial monitorg two monitorg locations Event se also show correct calibration results tend to underestimate water depth flows figures sewers, as exemplified s 9 itial losses (tersection depression storage) both models, because flow is itialized at losses (tersection depression storage) both because flow is itialized at at same itialtwo losses (tersection depression storage) models, both models, flow is itialized monitorg locations Event se figures alsobecause show correct calibration same time as observed In general, SD results tend to temporal pattern time observed data. data. In general, SD results tend to temporal pattern ispeak values sameas time as(tersection observed data. Indepression general, SDstorage) results tend to predict predict temporal pattern peak peakvalues values itial losses predict both models, because flow itialized at more accuracy. more accuracy. more accuracy. same time as observed data. In general, SD results tend to predict temporal pattern peak values more accuracy. 8. Predicted flow observed data data Barkgside gauge Event study. 8. Predicted flow observed Barkgside gauge Event Predicted flow observed data Barkgside gauge Event study. study. 8. Predicted flow observed data Barkgside gauge Event study. 9. Predicted water depth observed data Sewer gauge Event 9. Predicted water depth observed data Sewer gauge Event study Predicted waterstudy. depth observed data Sewer gauge Event 9. Predicted water depth observed data Sewer gauge Event study study.

11 Water 2016, 8, last considerations are generalized to all events simulated statistical analysis presented 10, based on followg dicators: Relative error (RE) peak ( 10, left column): RE pvmax obs Vmax res q {Vmax obs (1) where RE is relative error peak results (Vmax res ) compared to peak observed data (Vmax obs ). RE was applied to flow water depth peaks. Positive RE values dicate underestimation by peak results negative values imply overestimation. RE has advantage beg a tangible statistic that evaluates permance a critical parameter such as peak flow or water depth. It is important to note that very large RE can be obtaed when low values are evaluated, even if absolute difference peak is small. In general, all simulated events RE is higher than SD models, which means that underestimates results agast observed data. In sewer sensor, SD model predicted accurate water depths but overestimated flows. This can be due to location sensor a zone where turbulence can occur affect monitorg data accuracy. In model this variation does not occur, because both water depth flows results are smooned underestimated. In conclusion, while SD model captured water depth flow peaks, model underestimated se results. Coefficient determation (R 2 ) ( 10, middle column) Regression coefficient (β) ( 10, right column): Resultg from a simple lear regression analysis applied between each simulated results time series observed data. se two statistics provide an dication how well results replicate observed data, both terms pattern (R 2 ) accuracy (β). R 2 measure ranges from 0 to 1 describes how much observed data variability is accordg simulated results. In practical terms, R 2 provides a measurement similarity between patterns observed data time series simulated results time series, i.e., dicates how hydrodynamics are captured by model. regression coefficient, β, is employed to provide supplementary mation to R 2. β «1 represents good agreement magnitude observed data results time series; β ą 1 means results are overestimated agast observed data (by a factor β); β ă 1 means results are underestimated agast observed data (by a factor β). For most simulated events, R 2 is close to 1 both SD models, which implies that variations modelg results match observed data, i.e., models can capture hydrodynamics observed data. differences between SD models are not so evident, but models tend to have higher R 2, which suggest that y have potential to better represent dynamic behavior stormwater flows urban catchments. β is general closer to 1 SD model results, which dicates that SD results are matchg observed data more accurately than ones. exceptions results analysis can be noticed data Valentes Sewer sensors. For Valentes Sewer sensor, network elements tend to overestimate water depths SD model. For Sewer sensor, errors observed flow data due to turbulence also affect this dicator as verified RE. se two aspects are not verified model as it underestimates overall results. Combg R 2 β results, it can be concluded that SD models capture hydrodynamics registered SD model tend to capture magnitude observed data while model underestimates it.

12 Water 2016, 8, Water 2016, 8, Relative error peak (RE) Coefficient determation (R2) Regression coefficient (β) sewer Valente channel Valente sewer Barkgside Statistical analysis modelg results agast observed data data study Zona Zona Case Case Study Study analysis analysis Zona Zona study study was based was based on four on floodg four floodg events events which rafall which records rafall records photographic photographic records records floodg floodg Praça 8 de Praça Maio 8 de were Maio available. were available. rafall records rafall are records summarized are summarized Table 2, Table considerg 2, considerg average average rafall rafall entire entire catchment catchment area. area. balances balances presented presented 11 show 11 show distribution distribution between between modules modules all events all analyzed, events analyzed, accordance accordance to analysis to presented analysis presented bee bee study. In this study. study, In this models study, also tend models to have also higher tend water to have higher at water 2D overl surface at than 2D overl SD models, surface less than volume SD discharged models, by less volume outfalls discharged 1D sewer by network. outfalls However, 1D this sewer network. study However, differences between this SD study differences models are between not as significant SD as models are not as significant catchment. as This is because rafall catchment. events This selected is because caused floods rafall both events SD selected models, caused which floods creased both SD overl models, surface which creased SD model. overl re surface are also significant differences SD model. re runf are also significant caused by small differences differences runf buildgs area caused at boundary by small differences catchment buildgs area model. at boundary catchment model.

13 Water 2016, 8, Water 2016, 8, Table 2. Summary rafall records Zona study. Table 2. Summary rafall records Zona study. Water 2016, 8, 58 Total Average Maximum Return Event Duration rafall Total rafall Average Start End Maximum rafall period Return name* Event Duration (h) depth rafall tensity rafall Start End (mm/h) rafall period (yr.) name* Table 2. Summary rafall (h) records Zona (mm) depth study. (mm/h) tensity (mm/h) (yr.) 9 June June :30 (mm) (mm/h) :50 June p.m June p.m :30 Average Maximum 36.6 Total 22.0 Return 50 Event 25 October 2:50 p.m. Duration Rafall Start October p.m. End 2006 Rafall Rafall Period Name * 5.0 (h) Intensity :30 October a.m :30 October a.m (mm/h) Depth (mm) (yr.) (mm/h) :30 a.m. 21 September 5:30 a.m. 21 September 9 June June September September 2:50 P.M :30 P.M :10 p.m October : p.m. October :10 p.m :30 A.M. 24 December 5:205:30 p.m. 24 December 2013 A.M September December 2013 September December 6:40 a.m :10 P.M. 6: p.m. 5:20 P.M :40 a.m. 24 December December :00 p.m Note: *This code 6:40 represents A.M. yymmdd 6:00it P.M. is used next figures to reference se events. Note: *This code Note: represents * This code yymmdd represents yymmdd it is used next it is used figures to next reference figures se to reference events. se events. 11. Volumes balance Zona study model runs. 11. Volumes balance Zona study model runs. To vestigate where model retas water on surface, 12a presents differences To vestigate between where where SD maximum model model retas retas water water on surface, on surface, divided 12a by presents l 12a presents use differences groups. Larger between differences discrepancies between SD maximum are registered SD onzones surface covered divided on by buildgs by surface l divided (residential use groups. by areas). Larger l This discrepancies use groups. means that are Larger registered runf discrepancies retaed zonesare covered on registered by overl buildgs surface (residential covered by areas). models buildgs This due means (residential to buildg that runf areas). sgularities, is This retaed means as on exemplified that overl runf is surface retaed 12b, on models overl surface due topondg buildg surface on sgularities, roads models is not asa due exemplified significant to buildg problem, sgularities, opposite 12b, to as surface exemplified pondg on study. roads12b, is not a significant surface problem, on roads opposite is not to a significant problem, study. opposite to study. (a) (b) (a) (b) Differences runf on on overlsurface generatedby bysd SD models Zona 12. Differences runf on overl surface generated by SD models Zona study: (a) (a) runf runf distributedby byl luse usetypes; (b) (b) example runf Zona on study: (a) runf distributed to by l use types; (b) example runf retaed on overl surface models due to buildg sgularities. retaed on overl surface models due to buildg sgularities

14 Water Water2016, 2016,8,8, comparison floodplas generated Praça 8 de Maio is summarized Table 3 all comparison floodplas generated Praça 8 de Maio is summarized Table 3 events analyzed. In general, floodg are higher SD than model, water all events analyzed. In general, floodg are higher SD than model, water depth floodg areas follow same pattern. This means that as model stores more water depth floodg areas follow same pattern. This means that as model stores more water on overl surface, runf are retaed upstream areas do not get to lower on overl surface, runf are retaed upstream areas do not get to lower zones where water accumulates reality. predicted floods at Praça 8 de Maio were also zones where water accumulates reality. predicted floods at Praça 8 de Maio were also compared compared to photographic records floodplas, as presented 13 events to photographic records floodplas, as presented 13 events two highest two highest return periods. It can be concluded that floodg extent is well predicted SD return periods. It can be concluded that floodg extent is well predicted SD model, but model, but underestimated model. underestimated model Photo evidence (b) SD model (a) (d) (e) (f) model (c) Comparison Comparisonphotograph photographrecords records predicted predictedfloodplas floodplason onpraça Praça88de demaio, Maio,Zona Zona study. (a) Flood registered on Event , photo adapted from [38]; (b) Flood study. (a) Flood registered on Event , photo adapted from [38]; (b) Flood registered registered on Event , photo local Diário newspaper Diário de(c)coimbra; (c) generated Floodpla on Event , photo courtesy courtesy local newspaper de Coimbra; Floodpla generated by SD Event model ; Event (d) Floodpla SD Event by SD model (d)060609; Floodpla generatedgenerated by SDbymodel model Event ; ; (e) Floodpla SD Event (f) Floodpla generated by (e) Floodpla generatedgenerated by SDby model model Event ; (f) ; Floodpla generated by model Event model Event

15 Water 2016, 8, Water 2016, 8, Table 3. Summary modelg results on Praça 8 de Maio, Zona study. Table 3. Summary modelg results on Praça 8 de Maio, Zona study. Max water depth (m) Floodg volume (m Event 3 ) Floodg area (m 2 ) Max water Max Water depth Depth (m) (m) Floodg Floodg volume Volume (m Event (m 3 ) Floodg Area (m 2 ) EventSD SD 3 ) Floodg area (m SD 2 ) SD SD SD SD SD 68 SD Assessg Importance Surface Storage as a Function Rafall Magnitude 4.3. Assessg Importance Surface Storage as a Function Rafall Magnitude aementioned analyses analyses dicate dicate that, that, general, general, models models reta reta larger larger water aementioned analyses dicate that, general, models reta larger water on on overl overl surface surface as compared as compared to to correspondg correspondg SD setup. SD setup. Dependg Dependg on on study, water on overl surface as compared to correspondg SD setup. Dependg on study, volume volume retaed retaed can be can stored be stored surface surface depressions, depressions, as occurred as occurred study, study, volume retaed can be stored surface depressions, as occurred study, or it can or be it retaed can be retaed buildg buildg sgularities, sgularities, as occurred as occurred Zona Zona study. To analyze study. study, or it can be retaed buildg sgularities, as occurred Zona study. To analyze importance importance surface storage surface relation storage relation rafall tensity, to rafall an analysis tensity, based an on analysis To analyze importance surface storage relation to rafall tensity, an analysis design based storms on was design permed. storms was models permed. models study were tested study five were design tested storms five based on design storms was permed. models study were tested five design returns storms period (RP) returns 10, 20, period 30, 100 (RP) , years, 20, 30, 100 models 200 years, Zona models study Zona design storms returns period (RP) 10, 20, 30, years, models Zona were tested six study design were storms tested RP six design 2, 5, 10, storms 20, 50 RP 100 years. 2, 5, 10, 20, years. study were tested six design storms RP 2, 5, 10, 20, years. s present relative floodg each l use group type, calculated s present relative floodg each l use group type, calculated based on maximum water depth predicted at 2D mesh overl surface. It It can be based on maximum water depth predicted at 2D mesh overl surface. It can be concluded that percentage floodg floodg volume volume on on roads roads is higher is higher SD SD model model two two concluded that percentage floodg volume on roads is higher SD model two studies. studies. In both In both situations, situations, crease crease rafall rafall return period, return period, thus tensity, thus tensity, led to decrease led to studies. In both situations, crease rafall return period, thus tensity, led to decrease relative relative roads roads an crease an crease areas covered areas by covered buildgs by buildgs decrease relative roads an crease areas covered by buildgs both SD both models. SD models. both SD models. 14. Relative 14. Relative floodg floodg on each onl eachuse l group use group design storms design events, storms events, study. 14. Relative floodg on each l use group design storms events, study. study. 15. Relative floodg each l use group design storms events, Zona study. 15. Relative 15. Relative floodg floodg each l eachuse lgroup use group design design storms storms events, events, Zona Zona study. study. 16 shows difference between SD models predictg floodg 16 shows difference between SD models predictg floodg each l use group type study. In addition to crease relative each l use group type study. In addition to crease relative volume residential areas, as shown 14, differences floodg between SD volume residential areas, as shown 14, differences floodg between SD

16 Water 2016, 8, shows difference between SD models predictg floodg Water 2016, 8, each l use group type study. In addition to crease relative volume residential areas, as shown 14, differences floodg between SD models are similar residential areas all events. For roads, floodg volume models are similar residential areas all events. For roads, floodg volume decreases as decreases as water tends to accumulate more se zones leadg to higher SD water tends to accumulate more se zones leadg to higher SD model high model high rafall tensities. 17 presents same analysis applied to Zona rafall tensities. 17 presents same analysis applied to Zona study. In this study. In this catchment, crease rafall return period led to crease catchment, crease rafall return period led to crease difference between difference between SD both roads residential areas. This means that SD both roads residential areas. This means that crease rafall crease rafall tensity, higher are retaed on overl surface by tensity, higher are retaed on overl surface by model, as compared to model, as compared to SD simulations. SD simulations Differences on on floodg floodg each l each use l group use group design storms design events, storms events, study. study. 17. Differences on floodg each l use group design storms events, Zona study. To To assess assess importance importance volume volume that that is is retaed retaed on on overl overl surface, surface, s s analyze 19 analyze differences differences between between flow flow 1D network. 1D network. It can be verified It can be that verified differences that between differences SD between models SD rise models crease rise draage crease areas draage decrease areas higher decrease tensity rafalls. higher tensity However, rafalls. differences However, have differences significantly have distct significantly trends distct each trends study. each In study. In study, surface storage study, can surface be neglected storage can high be neglected tensity rafalls, high tensity convergg rafalls, to low percentages convergg to low all monitorg percentages pot locations. all monitorg In pot Zona locations. In study, Zona however, surface storage study, is however, significant surface downstream storage is significant monitorg pot downstream locations monitorg all rafall pot locations tensities tested. all While rafall tensities tested. study While surface storage is study maly related surface storage surface is maly depressions, related surface Zona depressions, catchment Zona surface storage catchment verified surface storage model is verified also related to buildgs model is sgularities, also related to buildgs absence sgularities, data about private absence draage data networks about private connections. draage networks connections.

17 Water 2016, 8, Water 2016, 8, Water 2016, 8, Differences 18. Differences on flow on flow 1D network 1D network design storms design events, storms events, study. 18. study. Differences on flow 1D network design storms events, study. 19. Differences on flow 1D network design storms events, Zona study. 19. Differences 19. Differences on flow on flow 1D network 1D network design design storms storms events, events, Zona Zona study. 5. Discussion study. Conclusions 5. Discussion This paper presented Conclusions a comparison between SD models usg two real studies 5. different Discussion This characteristics paper presented Conclusions a floodg comparison mechanisms. between SD Innovative models concepts usg were two proposed real studies model buildg different Thisprocess characteristics paper presented to establish a comparison floodg connections mechanisms. between SD between Innovative models concepts modules usg were SD two proposed real models. studies model different buildg models characteristics process were to generally establish floodg found mechanisms. connections to accurately between Innovative reta runf concepts modules were SD on proposed overl models. surface model buildg due to surface models processwere depressions, togenerally establish buildgs found connections to accurately sgularities, between reta runf modules lack SD representation overl models. private surface connections due to models surface to were depressions, sewer generally network. found buildgs This to accurately has sgularities, not been reta observed runf lack SD on representation model, overl sce surface runf private due is to directly connections surface discharged depressions, to sewer from buildgs subcatchments network. sgularities, This has to network not been nodes. lack observed While representation surface SD depressions model, private sce connections buildgs runf to is sgularities directly sewerdischarged network. are dependent This from has subcatchments not on been resolution observed to network overl SD nodes. model, While surface sce surface module, runf depressions is directly lack discharged connection buildgs from to sgularities subcatchments mor system are dependent relies to network on on nodes. resolution While on surface sewer overl depressions flow surface module. module, buildgs lack sgularities connection are dependent to In mor onoverl system resolution relies flow on module, resolution overl surface surface on depressions module, sewer flow are module. lack related connection tosurface mor overl system relies defition In resolution overl buildgs flow onsgularities module, sewer flow are surface module. dependent depressions defition are related buildg boundaries. surface overl In defition In overl buildgs study, flowsurface sgularities module, depressions surface are depressions dependent are ma on are related cause defition retag buildg surface water overl boundaries. on defition overl In surface buildgs sgularities differences study, surface are between dependent depressions SD are defition models ma can cause be buildg neglected retag boundaries. water high In tensity on overl rafall events. surface In Zona differences between study SD buildgs sgularities models can be accumulate neglected significant high tensity runf volume, rafall traducg events. In significant Zona differences study between buildgs SD sgularities models, even accumulate high significant tensity runf rafall volume, events. traducg significant differences between SD models, even high tensity rafall events.

18 Water 2016, 8, study, surface depressions are ma cause retag water on overl surface differences between SD models can be neglected high tensity rafall events. In Zona study buildgs sgularities accumulate significant runf volume, traducg significant differences between SD models, even high tensity rafall events. This implies that models are likely to be accurate highly urbanized areas dense buildgs zones characterized by several sgularities delimited private areas, which could reta runf. resolution sewer network data defes connections between overl flow sewer flow modules. In addition to typically available data public sewer network, as used analyzed studies, models should also clude mation on private networks connections that dra areas delimited by buildgs. However, se data are difficult to obta most studies can make sewer flow module very complex. An alternative is to defe model only open areas (out buildgs, e.g., roads green areas), combed SD approach or areas catchment. In any, settg up a combed SD model depends on study could require pre-processg to decide which areas should be SD or. It should be mentioned that overl module usually considers a mimum water depth threshold that can also traduce differences runf generation on models. Usually, a mimum water depth threshold defes wettg dryg mechanism numerical stability, presented models this threshold was considered 1 mm. If water depth at a given 2D surface element is below this limit, any water fallg over given element is stored it until threshold is reached, only mass conservation is considered. This threshold can crease depression storage both SD models can reduce runf generated by models events low rafall depths. However, rafall events tested this paper makes this volume significant. defed threshold is much lower than rafall depth storm events under consideration is smaller than depression storage considered SD subcatchments. In conclusion, physically based models are more realistic, avoidg simplifications spatial data aggregation hydrological models applied on a subcatchment level SD models. Neverless, necessary resolution accuracy available data requirements, eir to defe modules connections, hydrological characterization, or even to do a proper calibration, are significantly higher models. In s where detailed network data are not available overl surface data are not accurate or do not have necessary resolution, SD models are a recommended modelg approach. In near future, models will benefit from crease data availability ir resolution, as well as data sources. Acknowledgments: Rui Pa acknowledges fancial support from Fundação para a Ciência e Tecnologia-Mistério para a Ciência, Tecnologia e Enso Superior, Portugal (SFRH/BD/88532/2012). Susana Ochoa-Rodriguez acknowledges support Interreg IVB NWE RaGa project. This project has received fundg from European Union s Horizon 2020 research novation program under grant agreement No Special thanks are due to AC, Águas de Coimbra, E.M., Coimbra, Portugal providg rafall sewer data pilot location, to Innovyze, Wallgd, UK providg research licences InfoWorks ICM stware, to Diário de Coimbra providg photographic records floodg events. Author Contributions: Rui Daniel Pa, Susana Ochoa-Rodriguez, Nuno Eduardo Simões had origal ideas discussed all authors defed studies presented this manuscript. Rui Daniel Pa built hydraulic models toger Susana Ochoa-Rodriguez Nuno Eduardo Simões, collected data analyzed hydraulic results. manuscript was written by Rui Daniel Pa contribution from all co-authors. work presented is a part Rui Daniel Pa s Ph.D. which is supervised by Čedo Maksimović, Alfeu Sá Marques Ana Mijic. All authors read approved fal manuscript. Conflicts Interest: authors declare no conflict terest. foundg sponsors had no role design study; collection, analyses, or terpretation data; writg manuscript, decision to publish results.

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